spd-metrics-id: A Python Package for SPD-Aware Distance Metrics in Connectome Fingerprinting and Beyond
This provides a tool for researchers in fields like neuroscience and imaging who need standardized SPD distance computations, but it is incremental as it packages existing methods.
The authors tackled the problem of computing distances between symmetric positive-definite (SPD) matrices by developing spd-metrics-id, a Python package that provides a unified, extensible, and reproducible framework, supporting various geometry-aware metrics and demonstrating applicability in connectome fingerprinting and other domains.
We present spd-metrics-id, a Python package for computing distances and divergences between symmetric positive-definite (SPD) matrices. Unlike traditional toolkits that focus on specific applications, spd-metrics-id provides a unified, extensible, and reproducible framework for SPD distance computation. The package supports a wide variety of geometry-aware metrics, including Alpha-z Bures-Wasserstein, Alpha-Procrustes, affine-invariant Riemannian, log-Euclidean, and others, and is accessible both via a command-line interface and a Python API. Reproducibility is ensured through Docker images and Zenodo archiving. We illustrate usage through a connectome fingerprinting example, but the package is broadly applicable to covariance analysis, diffusion tensor imaging, and other domains requiring SPD matrix comparison. The package is openly available at https://pypi.org/project/spd-metrics-id/.